SMRs, PMRs and Survival Measures Principles of Epidemiology Lecture 3 Dona SchneiderDona Schneider,...
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Transcript of SMRs, PMRs and Survival Measures Principles of Epidemiology Lecture 3 Dona SchneiderDona Schneider,...
SMRs, PMRs and
Survival Measures
Principles of Epidemiology
Lecture 3Dona Schneider, PhD, MPH, FACE
Epidemiology (Schneider)
REVIEW: Adjusted Rates are Created Through Standardization
Standardization:
The process by which you derive a
summary figure to compare health
outcomes of groups
The process can be used for mortality,
natality, or morbidity data
Epidemiology (Schneider)
Standardization Examples
Direct Method requires
Age-specific rates in the sample population
The age of each case
The population-at-risk for each age group in the sample
Age structure (percentage of cases in each age group)
of a standard population
Summary figure is an AGE-ADJUSTED RATE
Epidemiology (Schneider)
Standardization: Age Adjustment (cont.)
Indirect method requires
Age structure of the sample population at risk
Total cases in the sample population (not ages of cases)
Age-specific rates for a standard population
Summary figure is a STANDARDIZED MORTALITY RATIO (SMR)
Epidemiology (Schneider)
Indirect Standardization Instead of a standard population structure, you utilize
a standard rate to adjust your sample
Indirect standardization does not require that you know the stratum-specific rates of your cases
The summary measure is the SMR or standardized mortality/morbidity ratio
SMR = Observed X 100
Expected
Epidemiology (Schneider)
Indirect Standardization (cont.)
An SMR of 100 or 100% means no
difference between the number of
outcomes in the sample population and
that which would be expected in the
standard population
Epidemiology (Schneider)
Total expected deaths per year: 2,083
(3) = (1) X (2)(2)(1)
1,27522,95355,56555-64
5648,21268,68745-54
1742,86860,83835-44
591,59437,03025-34
111,3837,98920-24
Expected Number of Deaths for Farmers and
Farm Managers per 1,000,000
Standard Death Rates per 1,000,000 (All Causes of Death)
Number of Farmers and Farm Managers
(Census, 1951)Age Group
Example: SMR for Male Farmers, England and Wales, 1951
Total observed deaths per year: 1,464
SMR = 1,464 X 100 = 70.3%
2,083
In 1951, male farmers in England and
Wales had a mortality rate 30 percent
lower than the comparably-aged
general population.
Epidemiology (Schneider)
SMR = Observed / Expected X 100
SMR (for 20–59 yr olds) = 436 / 181.09 X 100 = 241%
436181.09Totals
11231.9675.2342,49455-59
(4)(3) = (1) X (2)(2)(1)
17458.3256.82102,64945-54
9850.5533.96148,87035-44
2217.4121.5480,84530-34
2013.7116.1285,07725-29
109.1412.2674,59820-24
Observed Deaths from TBC in White Miners
Expected Deaths From TBC in White Miners if They Had the Same Risk as the General Population
Death Rate (per 100,000) for TBC in Males in the General Population
Estimated Population of White MinersAge
(yr)
SMR for Tuberculosis for White Miners Ages 20 to 59 Years,
United States, 1950
Epidemiology (Schneider)
In the United States in 1950, white miners
ages 20 to 59 years died of tuberculosis
almost 2.5 times as often as comparably-aged
males in the general population
Epidemiology (Schneider)
Individuals in a cohort may contribute different amounts of risk due to length of exposure (person-years)
Calculation of stratum or age-specific and total SMRs
SMR = O/E X100 = 179/88.15 X 100 = 203%
88.15
24.38
46.50
14.27
3.00
(4) = (2) X (3)
Exp
Study Cohort
2.03 179Total
1.9725.09754870-79
2.1112.43,7509860-69
1.896.12,3402750-59
2.002.51,200640-49
(1) / (4)(3)(2)(1)
SMR =
Reference Population
Rate per 1,000
Person-Years in TOTAL cohort
Number or outdomes of
interest (Obs)Age
(yr)
Epidemiology (Schneider)
Workers in this cohort were twice as likely to have the outcome of interest as the
general population
Those ages 60-69 had the highest age-specific SMR
Those ages 50-59 had the lowest age-specific SMR
Epidemiology (Schneider)
SMR’s (con’t) Sometimes exposures change over time and
individuals may have different amounts of exposure when they are in a cohort over multiple years
Example: Over a period of years, the manufacturing process of product X changed. The occupational
cohort involved in the processes had 58 deaths (we do not know their ages). Was this more or less than would be expected in the general population?
Stratify the cohort by known exposure periods
Epidemiology (Schneider)
9.5450.92,09855-64
6.7432.01,55255-64
4.7409.41,14455-64
0.111.254415-24
.0617.53,70225-34
1.944.24,38235-44
4.7157.72,96845-54
1958-1963
0.010.3415-24
0.418.82,20625-34
2.246.34,73735-44
6.8164.14,11445-54
42.9TOTAL
SMR = observed/expected x 100% = 58 / 42.9 x 100% = 135%
1953-1957
1948-1952
3.1150.82,02845-54
1.544.53,27535-44
0.617.73,42325-34
0.19.91,25015-24
Exp. Cancer Deaths
US White Male CA Deaths (per 100,000)
Person-years in Cohort
Age Group
Epidemiology (Schneider)
Persons in this cohort had the outcome
35% more often than would be expected in
the general population.
We could not calculate age-specific SMRs
without the ages of the cases.
If we have the ages of cases:
Epidemiology (Schneider) SMR = Obs / Exp X 100 = 15 / 12.9 X 100 = 116%
Exp = 12.92.60.90.930-34
1.52.31.725-29
0.30.91.8Age 20-24
Expected deaths = population rates x person-years / 1000
1.71.81.930-34
1.51.51.725-29
1.61.81.8Age 20-24
Population rates(per 1,000)
Obs = 1521030-34
24325-29
012Age 20-24
Observed Deaths
150050050030-34
10001500100025-29
2005001000Age 20-24
1980-841975-791970-74Person-years
Epidemiology (Schneider)
From these data you can compute
A total SMR (116%)
Age-specific SMRs (age 20-25, SMR = 100%)
Time period SMRs (1970-1974, SMR = 114%)
Age-specific and time period SMRs (age 20-24,
1970-74, SMR = 111%)
Epidemiology (Schneider)
SMRs
Expect a Healthy worker effect Occupational studies should have SMRs < 100
Workers tend to be healthier than the general population which comprises both healthy and unhealthy individuals
You cannot compare SMRs between studies -- only to the standard population
Epidemiology (Schneider)
Comparison of Rates
Hides subgroup differencesPermits group comparison
Magnitude depends on population standardControls confounders
Fictional rateProvides a summary figureAdjusted
No summary figureProvides detailed
information
Cumbersome if there are many subgroups
Controls for homogeneous
subgroupsSpecific
Readily calculable
Difficult to interpret because of differences in population structures
Actual Summary ratesCrude
DisadvantagesAdvantages
Epidemiology (Schneider)
In Summary:
One type of rate is not necessarily more important than another. Which you choose depends on the information sought.
Crude rates are often used to estimate the burden of disease and to plan health services.
To compare rates among subpopulations or for various causes, specific rates are preferred.
To compare the health of entire populations, adjusted rates are preferred because they allow for comparison of populations with different demographic structures.
Epidemiology (Schneider)
CDC Wonder
http://wonder.cdc.gov/
Epidemiology (Schneider)
Additional Outcome Measures
Proportionate Mortality Ratio
Proportionate Mortality Rate
Case Fatality Rate
Years of Potential Life Lost
Measures of Survival
Epidemiology (Schneider)
Additional Outcome Measures
Proportionate Mortality Ratio
The ratio of observed/expected deaths (in terms of proportions of deaths in the standard population) x 100
PMRs are explained similarly to SMRs
100% = no difference between groups
Epidemiology (Schneider)
Computing a PMR
All Deaths 1950-54 1955-59 1960-64
20-24 10 5 2
25-29 10 15 10
30-34 5 5 15
Cancer Deaths20-24 2 1 0
25-29 3 4 2 observed30-34 0 1 2 =15Population Proportion of Cancer Deaths
20-24 0.07 0.07 0.07
25-29 0.09 0.10 0.10
30-34 0.11 0.12 0.12
Expected deaths due to cancer = Population proportion x all deaths in sample20-24 0.7 0.4 0.1
25-29 0.9 1.5 1.0 expected30-34 0.6 0.6 1.8 =7.6
PMR = Observed/Expected x 100 = (15/7.6) x 100 = 197%
Epidemiology (Schneider)
PMR = 197%
The study population has twice the proportion
of cancer deaths as the standard population.
Epidemiology (Schneider)
CHD Proportionate Mortality RateFigure 3-19. Deaths from heart disease as a percent of deaths from all causes, by age group, United States, 1986.
0%
20%
40%
60%
80%
100%
AllAges
<1 1-4 5-14 15-24 25-34 35-44 45-54 55-64 65-74 75-84 85+
Pe
rce
nt
of
All
De
ath
s
Heart Disease All Other Causes
Epidemiology (Schneider)
2.71.52,211Diabetes mellitus9
5.43.04,449
Chronic liver disease and cirrhosis7
4.12.33,343Cerebrovascular diseases8
14.98.312,281Suicide6
100147,750All causes
2.71.52,203Pneumonia and influenza10
15.0 8.412,372Homicide and legal
intervention5
19.210.715,822Diseases of the heart4
26.414.721,747HIV infection3
27.015.022,228Malignant neoplasms2
32.218.026,526Accidents and adverse effects
1
Cause-specific death rate per 100,000
Proportionate mortality rate (%)
NumberCause of DeathRank
Order
Ten Leading Causes of Death, 25-44 Years, All Races, Both Sexes, United States, 1991 (Population 82,438,000)
Epidemiology (Schneider)
Comparing Mortality and Case-Fatality Rates
Assume a 1995 population of 100,000 people where 20 contract disease X and 18 people die from the disease. One remains stricken and one recovers. What is the mortality rate and what is the case-fatality rate for disease X?
Mortality rate from disease X18 / 100,000 = .00018 = .018%
Case-fatality rate from disease X18 / 20 = .9 = 90%
Epidemiology (Schneider)
Years of Potential Life Lost Death occurring in a particular individual at an
early age results in a greater loss of that
individual’s productivity than if that same
individual lived to an average life span.
By convention, YPLL (or PYLL) is based on a life
expectancy of 75 years
YPLL can be calculated for individual or group data
Epidemiology (Schneider)
Example: Individual data method A person who died at age 20 would contribute 55
potential years of life lost (75-20=55 YPLL)
Deaths in individuals 75 years or older are
excluded
The rate is obtained by dividing total potential
years of life lost by the total population less than
75 years of age.
Epidemiology (Schneider)
*excluded
YPLL from Disease X = 169.5 / 4 = 42.4 per person
169.5xxxSum
15605
xx85*4
60153
20552
74.56 months1
YPLL Contributed (75-age)
Age at Death (Years)
Individual
Epidemiology (Schneider)
Example: Age Group MethodIn a population of 12,975,615, what is the rate of YPLL for 2000?
1. Obtain the ages at the time of death for each case (column 1)Exclude those over age 75
2. Calculate the mean age for each age group (column 2)
3. Subtract the mean age from 75 (column 3)
4. Calculate stratum-specific YPLL by multiplying column 1 by column 3
5. Sum the stratum-specific YPLL
6. Divide by the total population for the ages selected
Epidemiology (Schneider) Rate of YPLL per 1,000 persons = 93,234.0/12,975,615 = 7.2 per 1,000 in 2000
6412.537.537.517135-39
xxx
2.5
7.5
12.5
17.5
22.5
27.5
32.5
42.5
47.5
52.5
57.5
62.5
67.5
72.0
74.5
Age 75-mean(3)
93,234.0xxxxxx
175.072.57070-74
480.067.56465-69
1075.062.58660-64
1487.557.58555-59
1912.552.58550-54
3190.047.511645-49
4257.542.513140-44
10327.532.524330-34
14630.027.530825-29
21525.022.541020-24
18112.517.531515-19
4000.012.56410-14
3510.07.5525-9
2016.03.0281-4
298.00.54<1
YPPL(1)x(3)
Mean Age at Death(2)
# Deaths(1)Age
Epidemiology (Schneider)
Measuring Survival
Five-year survival
Not a magical number
May be subject to LEAD TIME BIAS
Cannot evaluate new therapies
Epidemiology (Schneider)
Measuring Survival (cont.)
Life Tables (assume no change in treatment over the time of observation) Used to calculate probability of surviving fixed
segments of time
Allow each case to contribute to data analysis regardless of the time segment in which they are enrolled
The probability of surviving 5 years is the product of surviving each year (p.89)
Epidemiology (Schneider)
Measuring Survival (cont.) Kaplan-Meier
Time periods are not predetermined but are
set by the death or diagnosis of a case
Withdrawls and those lost to follow-up are
removed from the analysis
Typically used for small numbers of cases
Epidemiology (Schneider)
Measuring Survival (cont.)
Median Survival
The time that half the population survives
Not effected by outliers like the mean
Can calculate the median survival time
when half rather than all the cases die
Epidemiology (Schneider)
Measuring Survival (cont.)
Relative survival rate
Compares survival from a given disease to a
comparable group who do not have the
disease
Relative Survival Rate (%) = Observed/Expected x 100